Longitudinal Measurement Non-Invariance with Ordered-Categorical Indicators: How are the Parameters in Second-Order Latent Linear Growth Models Affected?. Issue 5 (3rd September 2018)

• Record Type: Journal Article
• Authors: Liu, Yu; West, Stephen G.
• Is Part Of: Structural equation modeling. Volume 25:Issue 5(2018)
• Page Start: 762
• Page End: 777
• Publication Date: 2018-09-03
• Subjects: longitudinal measurement non-invariance -- ordered-categorical indicators -- sensitivity analysis -- second-order latent growth model; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2017.1419353
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : Models of change typically assume longitudinal measurement invariance. Key constructs are often measured by ordered-categorical indicators (e.g., Likert scale items). If tests based on such indicators do not support longitudinal measurement invariance, it would be useful to gauge the practical significance of the detected non-invariance. The authors focus on the commonly used second-order latent growth curve model, proposing a sensitivity analysis that compares the growth parameter estimates from a model assuming the highest achieved level of measurement invariance to those from a model assuming a higher, incorrect level of measurement invariance as a measure of practical significance. A simulation study investigated the practical significance of non-invariance in different locations (loadings, thresholds, uniquenesses) in second-order latent linear growth models. The mean linear slope was affected by non-invariance in the loadings and thresholds, the intercept variance was affected by non-invariance in the uniquenesses, and the linear slope variance and intercept–slope covariance were affected by non-invariance in all three locations.